Abstract：
Acute Lymphoblastic Leukemia (ALL) is a life-threatening type of cancer wherein mortality rate is unquestionably high. Early detection of ALL can reduce both the rate of fatality as well as improve the diagnosis plan for patients. In this study, we developed the ALL Detector (ALLD), which is a deep learning-based network to distinguish ALL patients from healthy individuals based on blast cell microscopic images. We evaluated multiple DL-based models and the ResNet-based model performed the best with 98% accuracy in the classification task. We also compared the performance of ALLD against state-of-the-art tools utilized for the same purpose, and ALLD outperformed them all. We believe that ALLD will support pathologists to explicitly diagnose ALL in the early stages and reduce the burden on clinical practice overall.
摘要：
急性淋巴细胞白血病(ALL)是一种威胁生命的癌症,其中死亡率无疑很高。早期发现ALL既可以降低病死率，又可以改善患者的诊断计划。在这项研究中,我们开发了所有探测器（ALLD），这是一个基于深度学习的网络，根据原始细胞显微图像将所有患者与健康个体区分开来。我们评估了多个基于DL的模型，基于ResNet的模型在分类任务中表现最好，准确率为98%。我们还将ALLD的性能与用于相同目的的最先进的工具进行了比较，所有的人都胜过他们。我们相信，ALLD将支持病理学家在早期明确诊断ALL，并总体上减轻临床实践的负担。
